Top-k node identification method based on Gaussian plume model

Xu Cao, F. Yin
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引用次数: 0

Abstract

As one of the most commonly used models in the Top-k node recognition task, the greedy model has the advantages of convenience, easy understanding and stable effect. The CELF++ algorithm, as a method of using the greedy strategy, also has the above characteristics. However, since the algorithm uses Monte Carlo simulation to calculate the effect of node influence diffusion, its time overhead is unbearable on large networks. Regarding the above points, this paper introduces a Gaussian plume model commonly used in the field of atmospheric pollution diffusion simulation, and proposes a Gaussian influence diffusion model. On this basis, the CELF++ algorithm is improved, and the Gaussian influence diffusion model is used to replace the traditional Monte Carlo simulation to model the influence diffusion in social networks, and the GPM-CELF++ (Gaussian Plume Model-CELF++) algorithm is proposed. Extensive experimental results on real datasets show that the proposed algorithm has advantages in both propagation effect and running time compared with baseline methods.
基于高斯羽流模型的Top-k节点识别方法
贪心模型是Top-k节点识别任务中最常用的模型之一,具有方便、容易理解、效果稳定等优点。celf++算法作为一种使用贪心策略的方法,也具有上述特点。然而,由于该算法采用蒙特卡罗模拟来计算节点影响扩散的效果,其时间开销在大型网络上难以承受。针对以上几点,本文介绍了大气污染扩散模拟领域常用的高斯羽流模型,提出了高斯影响扩散模型。在此基础上,对celf++算法进行改进,用高斯影响扩散模型代替传统的蒙特卡罗仿真对社交网络中的影响扩散进行建模,提出了gpm - celf++(高斯羽流模型- celf++)算法。在实际数据集上的大量实验结果表明,与基线方法相比,该算法在传播效果和运行时间上都具有优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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